Emerging Trends in The Social and Behavioral Sciences

Cooperative Relationships

Media

Part of Cooperative Relationships

Title

Cooperative Relationships

extracted text

Cooperative Relationships
DANIEL J. HRUSCHKA and JOAN B. SILK

Abstract
Cooperative relationships arise from a history of mutually beneficial interactions
between individuals, and they enable cooperation among a range of entities, including biological organisms, business firms, and nation-states. As one of the simplest
of emergent social forms, cooperative relationships can possess higher level properties (e.g., common expectations and rules of interaction, shared communication
protocols) that are more than the sum of individual interactions. As such, cooperative relationships can become “things” in their own right, shaping how partners
treat each other and how others treat partners within a relationship. Many open
questions remain about how the emergent properties of cooperative relationships
arise and how they foster future beneficial interactions while mitigating the risk of
exploitation. Here, we frame these diverse findings and emerging questions in terms
of the inputs and algorithms that partners use in forming models of each other and
in guiding behaviors toward each other. We finish by outlining areas ripe for future
exploration.

INTRODUCTIONS
Cooperation is pervasive in the biological world. It has played a key
role in the major evolutionary transitions from prokaryotes to eukaryotes, from eukaryotes to multicellular organisms, and from multicellular
organisms to social animal colonies (West, Fisher, Gardner, & Kiers, 2015).
Cooperation at multiple scales is also a crucial ingredient in the meteoric
rise of humans on Earth (Boyd & Richerson, 2009). While cooperating
with others can confer benefits, it can also place cooperators at risk of
exploitation by selfish partners who might try to enjoy the benefits of
cooperation without paying any of the costs. Cooperative relationships
are one mechanism for mitigating such risk by increasing the probability
of beneficial interactions in the future and by increasing the efficiency of
these interactions. These changes in turn give partners additional incentives to continue cooperating and to maintain their relationship into the
future.
Emerging Trends in the Social and Behavioral Sciences.
Robert Scott and Marlis Buchmann (General Editors) with Stephen Kosslyn (Consulting Editor).
© 2017 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

1

2

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Cooperative relationships arise from a history of interactions, but they are
not a simple sum or description of those interactions (Hinde, 1976; Kummer, 1978). They also rely on emergent properties that can in turn shape
future interactions. These include expectations, rules of coordination, shared
modes of communication, commonly understood consequences for defection, and partners’ valuation of each other and the relationship (Delton &
Robertson, 2016; Hruschka, 2010). Such properties rely on a range of capacities permitting organisms to set up common expectations and protocols,
to resist the temptation to exploit a partner, to recognize good partners, to
evaluate the long-term benefits of a relationship, and to modulate behaviors
toward friends, strangers, freeloaders, and enemies based on these evaluations (Brent, Chang, Gariépy, & Platt, 2014; Hruschka, 2010).
As these capacities extend the possible length of relationships far into the
future, it becomes increasingly difficult to assess the long-term value of any
given relationship based on past behaviors. This can complicate decisions
about staying with existing partners versus leaving them for others who
might be more beneficial relationship partners in the long run. It can also
increase the salience of cues and signals of a partners’ commitment to
continuing the relationship in the long term (Hruschka, 2010). The spiraling
complexity required to orchestrate these decisions and behaviors may be
one reason that cooperative relationships do not arise more often in the
biological world. It also raises fascinating questions about the evolution and
development of such relationships. In this essay, we review some of the best
studied questions in this area and outline directions for future work.
FOUNDATIONAL
In the last two decades, researchers have shown that organisms from a wide
range of taxa selectively cultivate enduring relationships with conspecifics,
and that past histories of mutually beneficial interactions with a partner
predict future behaviors toward that partner (Seyfarth & Cheney, 2012).
These results parallel long-standing findings in the social sciences about
cooperative relationships between individuals as well as higher level social
organizations, such as business firms and nation-states (Hruschka, 2010;
Leeds, 2003; Ring & Van de Ven, 1992; Uzzi, 1997). In many cases, individuals
also appear to gain long-term benefits from their social relationships. These
include improvements in individual health and well-being and, for organizations, increased profits or greater success in accomplishing key missions
(Berkman & Glass, 2000; Dyer & Singh, 1998). Among organisms there is also
emerging evidence that social relationship can contribute to individual fitness, suggesting that the ability to cultivate and maintain such relationships
can give a selective advantage (Silk & National Research Council, 2014).

Cooperative Relationships

3

These common findings across a range of disciplines have given rise to
questions and debates about the origins and design of such cooperative
relationships. One can productively frame many of these questions in
computational terms using the related concepts of inputs and algorithms.
Inputs are cues and signals that individuals pay attention to when forming
models of a partner and a relationship. These can include the duration of
the relationship, the balance of past favors, information about genetic relatedness, and cues of how much a partner wants to continue the relationship
into the future. Algorithms are computational systems for transforming these
inputs into actions. These can involve evaluations and models of the relationship that have been shaped over a long history of interactions (Delton
& Robertson, 2016; Hruschka, 2010; Hruschka, Hackman, & Macfarlan,
2015; Tooby, Cosmides, Sell, Lieberman, & Sznycer, 2008). Importantly, these
algorithms may change over the course of a relationship as partners begin
to pay attention to different inputs or integrate those inputs in new ways
(Xue & Silk, 2012).
The computational perspective provides a unified way of asking a number of common questions about cooperative relationships. What inputs and
algorithms do partners use in decisions to start, build, maintain, and switch
cooperative relationships? How do these inputs and algorithms change as
partners move through different stages of a cooperative relationship? From
what physiological and cognitive building blocks did natural selection construct these algorithms? How can researchers assess these algorithms across
taxa in observational and experimental settings? And what consequences
do different algorithms have for the long-term robustness of such relationships?
Different fields have tackled these questions with different theoretical
assumptions about how these algorithms work and what inputs they rely on.
Human behavioral ecology, for example, has focused on theoretical inputs
traditionally derived from evolutionary models of cooperation—genetic
relatedness, spatial proximity, frequency of interaction, past helping behavior, and reproductive pair bonds—while paying less attention to proximate
psychological mediators (Hackman, Munira, Jesmin, & Hruschka, 2017).
This reflects twin assumptions that (i) these theoretical variables are the
relevant inputs informing behavior within relationships and (ii) the algorithms translating these inputs into helping are sufficiently direct that
investigating psychological mediators will likely not improve behavioral
models (Hackman et al., 2017).
Researchers studying nonhuman animals have also necessarily focused
on observable behavioral variables—tolerance, affiliation, grooming, coalitional support, and mating—in predicting future patterns of behaviors
in relationships. However, realizing that cooperative relationships often

4

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

rely on partner’s emergent expectations and evaluations of each other,
primatologists in particular have begun to develop novel behavioral and
experimental tools to assess these psychological mediators and their relationship to behaviors (Cords & Aureli, 2000; Dunbar & Shultz, 2010; Silk,
Cheney, & Seyfarth, 2013).
Meanwhile, economists and psychologists have examined how different
relational cues, perceptions, and evaluations guide behaviors within cooperative relationships (Aron, Aron, & Smollan, 1992; Berscheid, Snyder, &
Omoto, 1989; Jones & Rachlin, 2006). While useful at reverse engineering
relational algorithms, the experimental and survey methods used in these
social sciences often rely heavily on human language and the manipulation
of complex symbolic systems. They also frequently rely on behaviors in
laboratory contexts that have unknown external validity. For these reasons,
it can be challenging to extend these findings to diverse human groups
with varying levels of literacy and education, much less to nonhuman
organisms.
Given their complementary strengths and weaknesses, each of these
approaches can contribute useful methods and findings to the study of
cooperative relationships. In the next section, we outline some key advances
stemming from these diverse fields of study.
RECENT RESEARCH
What Inputs and Algorithms Do Partners Use in Decisions to Start, Build,
Maintain, and Switch Cooperative Relationships?
A large body of work has examined how past costs and benefits from a
relationship are associated with future behaviors. This includes short-term
studies of recent helping or sharing in laboratory experiments conducted
on humans and several nonhuman primate species, long-term field studies
of food sharing in humans and other primates, and surveys asking people
to evaluate the long-term balance of give-and-take in their relationships
(Brosnan et al., 2009; Hruschka, 2010; Jaeggi & Gurven, 2013). Researchers
have focused on these specific inputs because they are important variables in
game theoretic models of cooperation and because they give some indication
of the benefits one’s partner could provide in the future.
However, many factors can influence correlations between a partner’s
past behaviors and future behaviors. Most notably, a partner can find more
beneficial relationships or better outside options. Thus, paying attention to
cues of such changes in a partner can also be important in helping individuals avoid exploitation. In this regard, researchers have begun to explore
another set of inputs also derived directly from game theoretic models

Cooperative Relationships

5

of cooperation. In such models, inequalities between three variables—the
benefits and costs of cooperation and the expected duration of a series of
interactions—determines when it is reasonable to cooperate with another.
In most game theoretic formulations, the third variable—the expected
duration of the relationship—is defined a priori. However, partners can
often dramatically prolong or shorten a relationship by their own actions.
In such cases, individuals should be particularly attuned to their partner’s
commitment to continuing the relationship.
This second concern about the expected duration of a relationship has led
to exciting research in the last decade, showing that evaluations of a relationship depend on much more than the direct benefits one has received or
the costs one has incurred. Among other things, these evaluations depend on
cues that one’s partner can rely on a third party to satisfy the same needs as
one might provide (Niiya & Ellsworth, 2010). They can also depend on cues
that a partner is giving exclusive attention to the relationship (Ohtsubo et al.,
2014) or how a partner ranks the relationship relative to other possible relationships (DeScioli & Kurzban, 2009). What unifies these diverse cues and
signals is that each gives some indication of how much a partner values this
relationship specifically and how much that partner would work to extend it
into the future. Interestingly, having a partner who wants to prolong a beneficial relationship with you can also make that partner more valuable to you, a
process that can lead to mutually reinforcing evaluations among two partners
(Tooby & Cosmides, 1996).
Another area of active research aims to understand how individuals
encode the value of their partners and relationships and how these encodings, sometimes known as internal regulatory variables, shape future action.
Modeling work suggests that long-run encodings of a partner and the
partner’s contribution to one’s well-being can facilitate the emergence
of cooperative relationships (Hruschka & Henrich, 2006; Roberts, 2005).
There is also considerable theoretical work on what specific inputs should
contribute to these encodings, what “bookkeeping” styles transform these
inputs into encodings, and how these encodings interact with other information to guide decisions (Delton & Robertson, 2016; Hruschka et al., 2015;
Lieberman, Tooby, & Cosmides, 2007; Silk, 2003).
One concept that researchers from a range of fields have begun to explore
is perceived closeness, an assessment of relationship quality that in many
(but not all) cultures relies on a metaphor of spatial proximity (Aron et al.,
1992; Curry, Roberts, & Dunbar, 2013; Hackman et al., 2017; Jones & Rachlin,
2006). It is important to point out that perceived closeness is only loosely
correlated with actual spatial proximity. Rather, it is a general way of ranking relationships in terms of their positive value to an individual. Research
across neuroscience, economics, psychology, and anthropology has shown

6

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

that perceived closeness increases over a series of cooperative interactions
(Krueger et al., 2007), that cues that a partner devalues a relationship can
reduce feelings of closeness (Niiya & Ellsworth, 2010), and that perceived
closeness is associated with greater helping and sharing in experimental and
observational settings (Aron et al., 1992; Curry et al., 2013; Hackman et al.,
2017; Hruschka, 2010; Jones & Rachlin, 2006).
Although perceived closeness does somewhat correlate with a range
of other cues, such as genetic relatedness, past help, and frequency of
interaction, there is little evidence that perceived closeness can be reduced
to a simple mediator of other commonly studied variables. For example, the
associations of perceived closeness with helping are independent of other
important variables, such as genetic or affinal kinship, spatial proximity,
and help received from one’s partner (Hackman et al., 2017). Moreover, a
number of studies have shown that other important predictors, such as
genetic kinship and participation in a mating tie, have associations with
helping and sharing that are independent of perceived closeness (Curry
et al., 2013; Hackman, Danvers, & Hruschka, 2015). Thus, perceived closeness
appears to open a window to psychological mediators having independent
associations with cooperative behaviors.
Perceived closeness is one of the most thoroughly and widely studied
ways of encoding the value of a partner and relationship, but there are
many others, including judgments of competence, trustworthiness, genetic
kinship, mating and pair-bonding potential, and relative need, that likely
interact with closeness in guiding decisions about behavior toward partners
(Fiske, Cuddy, & Glick, 2007; Hackman et al., 2015; Rousseau, Sitkin, Burt, &
Camerer, 1998).
HOW CAN RESEARCHERS ASSESS THESE ALGORITHMS ACROSS CULTURE AND TAXA?
Taking an algorithmic approach to cooperative relationships has traditionally
required inferring relevant inputs and internal computations from observed
behaviors—attention, tolerance, affiliation, helping, and sharing (Silk et al.,
2013), and among humans, self-reports of internal states (Aron et al., 1992;
Hackman et al., 2017; Jones & Rachlin, 2006). Using averages and correlations
among these behaviors over time and behavioral responses to experimental
stimuli, researchers attempt to reverse engineer the relevant inputs, encodings, and algorithms that guide actions with relationship partners.
Among humans, self-reports about perceptions and evaluations provide
some window into the ways that individuals encode important information
about partners and relationships (Aron et al., 1992; Berscheid et al., 1989;
Jones & Rachlin, 2006). However, it is difficult to verify self-reports about
mental states. Moreover, it is not straightforward to elicit these self-reports

Cooperative Relationships

7

across diverse languages and cultures. For example, the increasingly
popular perceived closeness paradigm often uses spatial metaphors to elicit
rankings of relationships, but not all languages and cultures use spatial
metaphors to rank their relationships. And even when individuals may be
able to respond in terms of spatial metaphors, common two-dimensional
tasks on paper may not be immediately meaningful to respondents. In
the last 15 years, researchers have been developing and adapting different
approaches to tackle these problems and to develop a richer view of relational decision-making. These include experiments that probe attention,
processing speed, and decision-making about partners and relationships
(Karremans & Aarts, 2007; O’Gorman & Roberts, 2017; Wiltermuth & Heath,
2009), new methods for mapping brain networks involved in relational
decision-making (Krueger et al., 2007), and the development of locally
appropriate tools for assessing concepts such as emotional closeness and
their relationship with behavior in diverse cultural settings (Hackman et al.,
2017).
Researchers working with nonhuman animals have necessarily focused
on reverse engineering relevant inputs and algorithms from observable
behaviors and vocal signals. These include social proximity, social grooming, the amount of time spent in interactions, responses to separations and
reunions, reconciliation, behavioral synchrony, directed vocal exchanges,
and social monitoring, as well as researcher-derived indices of relationship
quality based on these behaviors (Cords & Aureli, 2000; Dunbar & Shultz,
2010; Silk et al., 2013). The challenge with many of these measures is that
they are both potentially inputs to and outcomes of decision-making algorithms. Thus, it can be difficult to determine whether these measures are
correlated with other relational outcomes because they are something that
individuals use as inputs to their future behaviors or simply because they
are both outcomes of underlying and unmeasured relational processes. For
example, Dunbar and Shultz argue that relationship longevity may not be
a good measure of relationship quality because it is a functional outcome
of relational processes (Dunbar & Shultz, 2010). However, it is also possible
that relationship longevity is a salient input in an individual’s evaluation of
a relationship that influences future decision-making. This may very likely
be the case in many human friendships (Hruschka, 2010). Unfortunately,
focusing solely on the correlations of relational length with other behavioral
outcomes cannot adjudicate between these two possibilities. Experimental
manipulation of social situations and partner behaviors, such as experiments
playing back calls from known partners, may provide one path forward in
determining what informs future decision-making in relationships and what
simply arises as an outcome of relational processes (Silk et al., 2013). Such
experimental approaches have been quite successful in human studies. And

8

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

in both human and nonhuman studies assays of hormonal mediators can
shed light on the internal processes underlying behaviors with relationship
partners (De Dreu, 2012).
PROMISING NEW DIRECTIONS
Progress in answering the broad questions outlined above will benefit from
greater cross-talk between the diverse biological, social, and behavioral fields
that have developed theories and methods for studying cooperative relationships. Among the many avenues for future research, we outline several that
should lead to novel insights into the origins, development, and persistence
of cooperative relationships.
THE TRANSITION TO A RELATIONSHIP
Numerous studies have shown that partners in long-term cooperative
relationships interact in very different ways than acquaintances. These
differences appear to rely on different inputs and algorithms for dealing
with a specific partner—for example, increasingly complex models of
one’s partner, common expectations and tools of coordination, increasing
preferences for the partner’s well-being, more concern about partner intent,
and less concern about individual acts of helping and reciprocation (Clark &
Mils, 1993; Hruschka, 2010; Xue, 2013; Xue & Silk, 2012). They can also lead
to different weighting of inputs. For example, partners in close relationships
might weight their own costs less and their partner’s benefits more than
strangers (McGuire, 2003). These changes involve a shift toward the relationship becoming an independent “thing” with emergent properties that
guide partners’ behaviors in the relationship. But what meta-inputs lead to
such a transition, and how does this transition take place? What cues and
signals do partners use to determine that their relationship is in a new state
and that they would benefit from different modes of decision-making? Some
research has shown, for example, that starting small and gradually raising
the stakes can be an effective route to increasing the levels of cooperation
in a relationship (Roberts & Renwick, 2003). However, it is not clear that
this captures the apparently qualitative changes that can occur in some
kinds of cooperative relationships (e.g., close friendships among humans)
(Hruschka, 2010; Xue & Silk, 2012).
THE ONTOGENY OF COOPERATIVE RELATIONSHIPS
In addition to the changes that occur within developing relationships,
individual organisms must also develop the capacities to cultivate and

Cooperative Relationships

9

maintain these relationships. Some evidence suggests that human children
across a range of settings follow similar trajectories between the ages of
7 and 15 in developing key understandings of shared expectations and
relational norms (Gummerum & Keller, 2008). At the same time, there can
be quite substantial variation in individual views about expectations for
a good friendship even within cultures (Hruschka, 2009). While studies
assessing actual relational behavior either experimentally or observationally
have mainly been restricted to populations in the United States, Europe,
and a few other high-income countries, this is now beginning to change.
Experimental work conducted in six different cultures, including several
small-scale societies, showed that children’s behavior in situations that
require self-sacrifice changes as children mature and varies across cultures.
Population-specific variation in altruistic behavior emerges during middle
childhood, and children converge toward the behavior of adults within
their groups (House et al., 2013). This raises important questions about the
generalizability of existing findings, and the degree to which the development of capacities for cooperative relationships relies on culturally specific
inputs.
CONDITIONS FAVORING COOPERATIVE RELATIONSHIPS
Cooperative relationships are only one of many ways to facilitate cooperation. Reputation and third-party punishment can help reinforce cooperation
among a wide range of partners (Fehr & Fischbacher, 2004; Milinski,
Semmann, & Krambeck, 2002). And modern markets backed by sophisticated legal systems permit individuals and organizations to cooperate in
relatively anonymous and arms-length interactions (Kranton, 1996). Under
what conditions do cooperative relationships become the more effective
option for facilitating cooperation? In biology, a great deal of modeling
and empirical work has examined these different systems in isolation, but
has not examined when we should see certain systems favored over others
(Hruschka & Henrich, 2006; Milinski et al., 2002; Noë & Hammerstein,
1995). Some modeling and experimental work in economics, sociology, and
anthropology has explored the social ecological conditions under which
rational individuals would devote effort to cultivating relationships versus
engaging in market-type exchanges (Kranton, 1996). Further work that
assesses these and other hypotheses using cross-cultural and cross-species
data should improve our understanding of the role of cooperative relationships in facilitating cooperation and also develop better integrated
theories of the conditions favoring different mechanisms for facilitating
cooperation.

10

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

FROM COOPERATIVE RELATIONSHIPS TO COOPERATIVE NETWORKS
Once cooperative relationships become emergent entities, then networks
of such relationships can become important in integrating larger groups of
individuals and creating novel opportunities for cooperation and exchange.
For the last several decades researchers have been exploring the emergent
properties of these networks and how they influence individual behaviors
and outcomes. These include the advantages in accessing information,
support, and economic resources by actors in specific network positions
(Burt, 2009; Jackson, 2008), the ways that network structure can facilitate
or discourage cooperation among dyads (Fehl, van der Post, & Semmann,
2011), and the role that network structure and density play in cumulative
cultural evolution (Chapais, 2009; Hill, Wood, Baggio, Hurtado, & Boyd,
2014). Social network analyses have also become an increasingly important
tool for mapping and measuring the structure of connections in nonhuman
taxa (Croft, James, & Krause, 2008).
CULTURAL TOOLS AND EVOLVED PSYCHOLOGY
Comparative work suggests that the capacity and motivation to cultivate
and maintain long-term cooperative relationships is a human universal,
and may be characteristic of the common ancestors of many primate species
(Hruschka, 2010; Seyfarth & Cheney, 2012). Irrespective of whether or not
this reflects psychological mechanisms that were selected specifically for the
cultivation of cooperative relationships, we do know that humans often rely
on cultural tools to build, regulate, and maintain these relationships. There
are numerous disparate examples of such cultural tools, including formal
written contracts between firms, material artifacts used as enduring symbols
for a relationship, stories and memories used to remind partners of the value
of the relationship, and rituals used to create community enforcement and
fear of supernatural punishment (Hruschka, 2010). Conversely, research on
culturally dependent relationships, such as contract-based cooperative ties
between firms, also shows that such relationships frequently rely on key
stakeholders who use common human capacities to cultivate friendships,
loyalty, and commitment (Uzzi, 1997). Thus, particularly in the human case,
a productive question is how do pre-existing capacities interact with cultural
tools to permit novel cooperative relationships between individuals as well
as higher level entities such as tribes, business firms, and nation-states?
In closing, the biological, social, and behavioral sciences have built promising empirical and theoretical inroads for studying the emergent properties of
cooperative relationships and how these emergent properties shape future
behaviors and outcomes. However, substantial progress can be made by
comparing and cross-fertilizing methods and theories across these diverse

Cooperative Relationships

11

fields. Here, we outline two potentially unifying concepts—inputs and
algorithms—and illustrate how one can productively frame past findings
and new directions in terms of these concepts. These and other tools for
building bridges across the rich bodies of work on cooperative relationships
should contribute to answering key questions and opening up new directions
about how such relationships emerge and shape individual behavior.
REFERENCES
Aron, A., Aron, E. N., & Smollan, D. (1992). Inclusion of other in the self scale and
the structure of interpersonal closeness. Journal of Personality and Social Psychology,
63, 596.
Berkman, L. F., & Glass, T. (2000). Social integration, social networks, social support,
and health. Social Epidemiology, 1, 137–173.
Berscheid, E., Snyder, M., & Omoto, A. M. (1989). The relationship closeness inventory: Assessing the closeness of interpersonal relationships. Journal of Personality
and Social Psychology, 57, 792.
Boyd, R., & Richerson, P. J. (2009). Culture and the evolution of human cooperation.
Philosophical Transactions of the Royal Society of London B: Biological Sciences, 364,
3281–3288.
Brent, L. J., Chang, S. W., Gariépy, J. F., & Platt, M. L. (2014). The neuroethology of
friendship. Annals of the New York Academy of Sciences, 1316, 1–17.
Brosnan, S. F., Silk, J. B., Henrich, J., Mareno, M. C., Lambeth, S. P., & Schapiro, S. J.
(2009). Chimpanzees (Pan troglodytes) do not develop contingent reciprocity in an
experimental task. Animal Cognition, 12, 587–597.
Burt, R. S. (2009). Structural holes: The social structure of competition. Cambridge, MA:
Harvard University Press.
Chapais, B. (2009). Primeval kinship: How pair-bonding gave birth to human society. Cambridge, MA: Harvard University Press.
Clark, M. S., & Mils, J. (1993). The difference between communal and exchange
relationships: What it is and is not. Personality and Social Psychology Bulletin, 19,
684–691.
Cords, M., & Aureli, F. (2000). Reconciliation and relationship qualities. In F. Aureli
& F. B. M. de Waal (Eds.), Natural conflict resolution (pp. 177–198). Berkeley: University of California Press.
Croft, D. P., James, R., & Krause, J. (2008). Exploring animal social networks. Princeton,
NJ: Princeton University Press.
Curry, O., Roberts, S. G., & Dunbar, R. I. (2013). Altruism in social networks: Evidence
for a ‘kinship premium’. British Journal of Psychology, 104, 283–295.
De Dreu, C. K. (2012). Oxytocin modulates cooperation within and competition
between groups: An integrative review and research agenda. Hormones and Behavior, 61, 419–428.
Delton, A. W., & Robertson, T. E. (2016). How the mind makes welfare tradeoffs:
Evolution, computation, and emotion. Current Opinion in Psychology, 7, 12–16.

12

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

DeScioli, P., & Kurzban, R. (2009). The alliance hypothesis for human friendship.
PLoS One, 4, e5802.
Dunbar, R. I., & Shultz, S. (2010). Bondedness and sociality. Behaviour, 147,
775–803.
Dyer, J. H., & Singh, H. (1998). The relational view: Cooperative strategy and sources
of interorganizational competitive advantage. Academy of Management Review, 23,
660–679.
Fehl, K., van der Post, D. J., & Semmann, D. (2011). Co-evolution of behaviour
and social network structure promotes human cooperation. Ecology Letters, 14,
546–551.
Fehr, E., & Fischbacher, U. (2004). Third-party punishment and social norms. Evolution and Human Behavior, 25, 63–87.
Fiske, S. T., Cuddy, A. J., & Glick, P. (2007). Universal dimensions of social cognition:
Warmth and competence. Trends in Cognitive Sciences, 11, 77–83.
Gummerum, M., & Keller, M. (2008). Affection, virtue, pleasure, and profit: Developing an understanding of friendship closeness and intimacy in western and Asian
societies. International Journal of Behavioral Development, 32, 218–231.
Hackman, J., Danvers, A., & Hruschka, D. J. (2015). Closeness is enough for friends,
but not mates or kin: Mate and kinship premiums in India and US. Evolution and
Human Behavior, 36, 137–145.
Hackman, J., Munira, S., Jesmin, K., & Hruschka, D. J. (2017). Revisiting psychology
mechanisms in the anthropological study of altruism. Human Nature, 28(1), 76–91.
Hill, K. R., Wood, B. M., Baggio, J., Hurtado, A. M., & Boyd, R. T. (2014).
Hunter-gatherer inter-band interaction rates: Implications for cumulative culture.
PLoS One, 9, e102806.
Hinde, R. A. (1976). Interactions, relationships and social structure. Man, 11, 1–17.
House, B. R., Silk, J. B., Henrich, J., Barrett, H. C., Scelza, B. A., Boyette, A. H., …
Laurence, S. (2013). Ontogeny of prosocial behavior across diverse societies. Proceedings of the National Academy of Sciences, 110, 14586–14591.
Hruschka, D. (2009). Defining cultural competence in context: Dyadic norms of
friendship among US high school students. Ethos, 37, 205–224.
Hruschka, D. J. (2010). Friendship: Development, ecology, and evolution of a relationship
(Vol. 5). Berkeley: Univ of California Press.
Hruschka, D., Hackman, J., & Macfarlan, S. (2015). Why do humans help their
friends? Proximal and ultimate hypotheses from evolutionary theory. In V.
Zeigler-Hill, L. L. M. Welling & T. K. Shackelford (Eds.), Evolutionary perspectives
on social psychology (pp. 255–266). New York, NY: Springer.
Hruschka, D. J., & Henrich, J. (2006). Friendship, cliquishness, and the emergence of
cooperation. Journal of Theoretical Biology, 239, 1–15.
Jackson, M. O. (2008). Social and economic networks (Vol. 3). Princeton, NJ: Princeton
University Press.
Jaeggi, A. V., & Gurven, M. (2013). Reciprocity explains food sharing in humans and
other primates independent of kin selection and tolerated scrounging: A phylogenetic meta-analysis. Proceedings of the Royal Society of London B: Biological Sciences,
280, 20131615.

Cooperative Relationships

13

Jones, B., & Rachlin, H. (2006). Social discounting. Psychological Science, 17,
283–286.
Karremans, J. C., & Aarts, H. (2007). The role of automaticity in determining the inclination to forgive close others. Journal of Experimental Social Psychology, 43, 902–917.
Kranton, R. E. (1996). Reciprocal exchange: A self-sustaining system. The American
Economic Review, 86(4), 830–851.
Krueger, F., McCabe, K., Moll, J., Kriegeskorte, N., Zahn, R., Strenziok, M., … Grafman, J. (2007). Neural correlates of trust. Proceedings of the National Academy of
Sciences, 104, 20084–20089.
Kummer, H. (1978). On the value of social relationships to nonhuman primates: A
heuristic scheme. Social Science Information/sur les sciences sociales, 17(4–5), 687–705.
Leeds, B. A. (2003). Alliance reliability in times of war: Explaining state decisions to
violate treaties. International Organization, 57, 801–827.
Lieberman, D., Tooby, J., & Cosmides, L. (2007). The architecture of human kin detection. Nature, 445, 727–731.
McGuire, A. M. (2003). “It was nothing”-Extending evolutionary models of altruism
by two social cognitive biases in judgments of the costs and benefits of helping.
Social Cognition, 21, 363.
Milinski, M., Semmann, D., & Krambeck, H.-J. (2002). Reputation helps solve the
‘tragedy of the commons’. Nature, 415, 424–426.
Niiya, Y., & Ellsworth, P. C. (2010). Acceptability of favor requests in the United States
and Japan. Journal of Cross-Cultural Psychology, 43, 273–285.
Noë, R., & Hammerstein, P. (1995). Biological markets. Trends in Ecology & Evolution,
10, 336–339.
O’Gorman, R., & Roberts, R. (2017). Distinguishing family from friends: Implicit cognitive differences regarding general dispositions, attitude similarity, and group
membership. Human Nature.
Ohtsubo, Y., Matsumura, A., Noda, C., Sawa, E., Yagi, A., & Yamaguchi, M. (2014).
It’s the attention that counts: Interpersonal attention fosters intimacy and social
exchange. Evolution and Human Behavior, 35, 237–244.
Ring, P. S., & Van de Ven, A. H. (1992). Structuring cooperative relationships between
organizations. Strategic Management Journal, 13, 483–498.
Roberts, G. (2005). Cooperation through interdependence. Animal Behaviour, 70,
901–908.
Roberts, G., & Renwick, J. S. (2003). The development of cooperative relationships:
An experiment. Proceedings of the Royal Society of London B: Biological Sciences, 270,
2279–2283.
Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after
all: A cross-discipline view of trust. Academy of Management Review, 23, 393–404.
Seyfarth, R. M., & Cheney, D. L. (2012). The evolutionary origins of friendship.
Annual Review of Psychology, 63, 153–177.
Silk, J. B. (2003). Cooperation without counting. Genetic and Cultural Evolution of Cooperation, 37–54.
Silk, J. B. (2014). Evolutionary perspectives on the links between close social bonds,
health, and fitness. In M. Weinstein & M. A. Lane (Eds.), Committee on population,

14

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

division of behavioral and social sciences and education. Washington D.C.: The National
Academies Press.
Silk, J., Cheney, D., & Seyfarth, R. (2013). A practical guide to the study of
social relationships. Evolutionary Anthropology: Issues, News, and Reviews, 22,
213–225.
Tooby, J., & Cosmides, L. (1996). Friendship and the banker’s paradox: Other pathways to the evolution of adaptations for altruism. Proceedings of the British Academy,
88, 119–144.
Tooby, J., Cosmides, L., Sell, A., Lieberman, D., & Sznycer, D. (2008). Internal regulatory variables and the design of human motivation: A computational and evolutionary approach. In A. J. Elliot (Ed.), Handbook of approach and avoidance motivation
(pp. 251–271). Mahwah, NJ: Lawrence Erlbaum.
Uzzi, B. (1997). Social structure and competition in interfirm networks: The paradox
of embeddedness. Administrative Science Quarterly, 42(1), 35–67.
West, S. A., Fisher, R. M., Gardner, A., & Kiers, E. T. (2015). Major evolutionary
transitions in individuality. Proceedings of the National Academy of Sciences, 112,
10112–10119.
Wiltermuth, S. S., & Heath, C. (2009). Synchrony and cooperation. Psychological Science, 20, 1–5.
Xue, M. (2013). Altruism and reciprocity among friends and kin in a Tibetan village.
Evolution and Human Behavior, 34, 323–329.
Xue, M., & Silk, J. B. (2012). The role of tracking and tolerance in relationship among
friends. Evolution and Human Behavior, 33, 17–25.

DANIEL HRUSCHKA SHORT BIOGRAPHY
Daniel J. Hruschka is an Associate Professor of Anthropology and Global
Health at Arizona State University. Among other interests, he studies how
humans choose to help others, how they cultivate relationships that promote
cooperation, and when they choose to exclude outsiders. He is author of
the book, “Friendship: Development, Ecology and Evolution of a Relationship”. He directs the Laboratory of Culture Change and Behavior and his
research has been funded by the National Science Foundation and the Templeton Foundation.
JOAN SILK SHORT BIOGRAPHY
Joan B. Silk. I am interested in how natural selection shapes the evolution
of social behavior in primates. Most of my empirical work has focused
on the behavioral and reproductive strategies of female baboons (Papio
cynocephalus, Papio ursinus). Recent work documents the adaptive benefits
females derive from close social bonds. I am particularly interested in
questions that explicitly link studies of nonhuman primates to humans.

Cooperative Relationships

15

Experimental work conducted with chimpanzees and children focuses
on the phylogenetic origins and ontogenetic development of prosocial
preferences.
RELATED ESSAYS
Emergence of Stratification in Small Groups (Sociology), Noah Askin et al.
Aggression and Victimization (Psychology), Sheri Bauman and Aryn Taylor
Empathy Gaps between Helpers and Help-Seekers: Implications for Cooperation (Psychology), Vanessa K. Bohns and Francis J. Flynn
Close Friendships among Contemporary People (Sociology), Matthew E.
Brashears and Laura Aufderheide Brashears
Neighborhoods and Cognitive Development (Psychology), Jondou Chen and
Jeanne Brooks-Gunn
Misinformation and How to Correct It (Psychology), John Cook et al.
Bullying, Aggression, and Human Development (Psychology), Samuel E.
Ehrenreich and Marion K. Underwood
Diversity in Groups (Sociology), Catarina R. Fernandes and Jeffrey T. Polzer
Cognitive Processes Involved in Stereotyping (Psychology), Susan T. Fiske
and Cydney H. Dupree
State of the Art in Competition Research (Psychology), Márta Fülöp and
Gábor Orosz
Interdependence, Development, and Interstate Conflict (Political Science),
Erik Gartzke
Family Relationships and Development (Psychology), Joan E. Grusec
Food Sharing (Anthropology), Michael Gurven and Adrian V. Jaeggi
Grandmothers and the Evolution of Human Sociality (Anthropology), Kristen
Hawkes and James Coxworth
Group Identity and Political Cohesion (Political Science), Leonie Huddy
The Development of Social Trust (Psychology), Vikram K. Jaswal and Marissa
B. Drell
Social Network Analysis in the Study of Ethnic Inequalities (Psychology),
Frank Kalter
Reconciliation and Peace-Making: Insights from Studies on Nonhuman
Animals (Anthropology), Sonja E. Koski
Cooperative Breeding and Human Evolution (Anthropology), Karen L.
Kramer
Exploring Opportunities in Cultural Diversity (Political Science), David D.
Laitin and Sangick Jeon
Empirical Models of Bilateral Contracting (Economics), Robin S. Lee
Against Game Theory (Psychology), Gale M. Lucas et al.
Emotion and Intergroup Relations (Psychology), Diane M. Mackie et al.

16

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Social-Emotional Responding: A Perspective from Developmental Psychology (Psycholocy), Tina Malti, Stefania Sette, and Sebastian P. Dys
Domestic Political Institutions and Alliance Politics (Political Science),
Michaela Mattes
Political Psychology and International Conflict (Sociology), Rose McDermott
Implicit Attitude Measures (Psychology), Gregory Mitchell and Philip E.
Tetlock
Why Do Governments Abuse Human Rights? (Political Science), Will H.
Moore and Ryan M. Welch
A Bio-Social-Cultural Approach to Early Cognitive Development: Entering
the Community of Minds (Psychology), Katherine Nelson
Perceptions of the Legitimacy of Collective Decisions (Political Science),John
W. Patty
Class, Cognition, and Face-to-Face Interaction (Sociology), Lauren A. Rivera
The Economics of Conflict and Peace (Economics), Dominic Rohner
Creativity in Teams (Psychology), Leigh L. Thompson and Elizabeth Ruth
Wilson
Social Neuroendocrine Approaches to Relationships (Sociology), Sari M. van
Anders and Peter B. Gray
Theory of Mind (Psychology), Henry Wellman



Cooperative Relationships
DANIEL J. HRUSCHKA and JOAN B. SILK

Abstract
Cooperative relationships arise from a history of mutually beneficial interactions
between individuals, and they enable cooperation among a range of entities, including biological organisms, business firms, and nation-states. As one of the simplest
of emergent social forms, cooperative relationships can possess higher level properties (e.g., common expectations and rules of interaction, shared communication
protocols) that are more than the sum of individual interactions. As such, cooperative relationships can become “things” in their own right, shaping how partners
treat each other and how others treat partners within a relationship. Many open
questions remain about how the emergent properties of cooperative relationships
arise and how they foster future beneficial interactions while mitigating the risk of
exploitation. Here, we frame these diverse findings and emerging questions in terms
of the inputs and algorithms that partners use in forming models of each other and
in guiding behaviors toward each other. We finish by outlining areas ripe for future
exploration.

INTRODUCTIONS
Cooperation is pervasive in the biological world. It has played a key
role in the major evolutionary transitions from prokaryotes to eukaryotes, from eukaryotes to multicellular organisms, and from multicellular
organisms to social animal colonies (West, Fisher, Gardner, & Kiers, 2015).
Cooperation at multiple scales is also a crucial ingredient in the meteoric
rise of humans on Earth (Boyd & Richerson, 2009). While cooperating
with others can confer benefits, it can also place cooperators at risk of
exploitation by selfish partners who might try to enjoy the benefits of
cooperation without paying any of the costs. Cooperative relationships
are one mechanism for mitigating such risk by increasing the probability
of beneficial interactions in the future and by increasing the efficiency of
these interactions. These changes in turn give partners additional incentives to continue cooperating and to maintain their relationship into the
future.
Emerging Trends in the Social and Behavioral Sciences.
Robert Scott and Marlis Buchmann (General Editors) with Stephen Kosslyn (Consulting Editor).
© 2017 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

1

2

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Cooperative relationships arise from a history of interactions, but they are
not a simple sum or description of those interactions (Hinde, 1976; Kummer, 1978). They also rely on emergent properties that can in turn shape
future interactions. These include expectations, rules of coordination, shared
modes of communication, commonly understood consequences for defection, and partners’ valuation of each other and the relationship (Delton &
Robertson, 2016; Hruschka, 2010). Such properties rely on a range of capacities permitting organisms to set up common expectations and protocols,
to resist the temptation to exploit a partner, to recognize good partners, to
evaluate the long-term benefits of a relationship, and to modulate behaviors
toward friends, strangers, freeloaders, and enemies based on these evaluations (Brent, Chang, Gariépy, & Platt, 2014; Hruschka, 2010).
As these capacities extend the possible length of relationships far into the
future, it becomes increasingly difficult to assess the long-term value of any
given relationship based on past behaviors. This can complicate decisions
about staying with existing partners versus leaving them for others who
might be more beneficial relationship partners in the long run. It can also
increase the salience of cues and signals of a partners’ commitment to
continuing the relationship in the long term (Hruschka, 2010). The spiraling
complexity required to orchestrate these decisions and behaviors may be
one reason that cooperative relationships do not arise more often in the
biological world. It also raises fascinating questions about the evolution and
development of such relationships. In this essay, we review some of the best
studied questions in this area and outline directions for future work.
FOUNDATIONAL
In the last two decades, researchers have shown that organisms from a wide
range of taxa selectively cultivate enduring relationships with conspecifics,
and that past histories of mutually beneficial interactions with a partner
predict future behaviors toward that partner (Seyfarth & Cheney, 2012).
These results parallel long-standing findings in the social sciences about
cooperative relationships between individuals as well as higher level social
organizations, such as business firms and nation-states (Hruschka, 2010;
Leeds, 2003; Ring & Van de Ven, 1992; Uzzi, 1997). In many cases, individuals
also appear to gain long-term benefits from their social relationships. These
include improvements in individual health and well-being and, for organizations, increased profits or greater success in accomplishing key missions
(Berkman & Glass, 2000; Dyer & Singh, 1998). Among organisms there is also
emerging evidence that social relationship can contribute to individual fitness, suggesting that the ability to cultivate and maintain such relationships
can give a selective advantage (Silk & National Research Council, 2014).

Cooperative Relationships

3

These common findings across a range of disciplines have given rise to
questions and debates about the origins and design of such cooperative
relationships. One can productively frame many of these questions in
computational terms using the related concepts of inputs and algorithms.
Inputs are cues and signals that individuals pay attention to when forming
models of a partner and a relationship. These can include the duration of
the relationship, the balance of past favors, information about genetic relatedness, and cues of how much a partner wants to continue the relationship
into the future. Algorithms are computational systems for transforming these
inputs into actions. These can involve evaluations and models of the relationship that have been shaped over a long history of interactions (Delton
& Robertson, 2016; Hruschka, 2010; Hruschka, Hackman, & Macfarlan,
2015; Tooby, Cosmides, Sell, Lieberman, & Sznycer, 2008). Importantly, these
algorithms may change over the course of a relationship as partners begin
to pay attention to different inputs or integrate those inputs in new ways
(Xue & Silk, 2012).
The computational perspective provides a unified way of asking a number of common questions about cooperative relationships. What inputs and
algorithms do partners use in decisions to start, build, maintain, and switch
cooperative relationships? How do these inputs and algorithms change as
partners move through different stages of a cooperative relationship? From
what physiological and cognitive building blocks did natural selection construct these algorithms? How can researchers assess these algorithms across
taxa in observational and experimental settings? And what consequences
do different algorithms have for the long-term robustness of such relationships?
Different fields have tackled these questions with different theoretical
assumptions about how these algorithms work and what inputs they rely on.
Human behavioral ecology, for example, has focused on theoretical inputs
traditionally derived from evolutionary models of cooperation—genetic
relatedness, spatial proximity, frequency of interaction, past helping behavior, and reproductive pair bonds—while paying less attention to proximate
psychological mediators (Hackman, Munira, Jesmin, & Hruschka, 2017).
This reflects twin assumptions that (i) these theoretical variables are the
relevant inputs informing behavior within relationships and (ii) the algorithms translating these inputs into helping are sufficiently direct that
investigating psychological mediators will likely not improve behavioral
models (Hackman et al., 2017).
Researchers studying nonhuman animals have also necessarily focused
on observable behavioral variables—tolerance, affiliation, grooming, coalitional support, and mating—in predicting future patterns of behaviors
in relationships. However, realizing that cooperative relationships often

4

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

rely on partner’s emergent expectations and evaluations of each other,
primatologists in particular have begun to develop novel behavioral and
experimental tools to assess these psychological mediators and their relationship to behaviors (Cords & Aureli, 2000; Dunbar & Shultz, 2010; Silk,
Cheney, & Seyfarth, 2013).
Meanwhile, economists and psychologists have examined how different
relational cues, perceptions, and evaluations guide behaviors within cooperative relationships (Aron, Aron, & Smollan, 1992; Berscheid, Snyder, &
Omoto, 1989; Jones & Rachlin, 2006). While useful at reverse engineering
relational algorithms, the experimental and survey methods used in these
social sciences often rely heavily on human language and the manipulation
of complex symbolic systems. They also frequently rely on behaviors in
laboratory contexts that have unknown external validity. For these reasons,
it can be challenging to extend these findings to diverse human groups
with varying levels of literacy and education, much less to nonhuman
organisms.
Given their complementary strengths and weaknesses, each of these
approaches can contribute useful methods and findings to the study of
cooperative relationships. In the next section, we outline some key advances
stemming from these diverse fields of study.
RECENT RESEARCH
What Inputs and Algorithms Do Partners Use in Decisions to Start, Build,
Maintain, and Switch Cooperative Relationships?
A large body of work has examined how past costs and benefits from a
relationship are associated with future behaviors. This includes short-term
studies of recent helping or sharing in laboratory experiments conducted
on humans and several nonhuman primate species, long-term field studies
of food sharing in humans and other primates, and surveys asking people
to evaluate the long-term balance of give-and-take in their relationships
(Brosnan et al., 2009; Hruschka, 2010; Jaeggi & Gurven, 2013). Researchers
have focused on these specific inputs because they are important variables in
game theoretic models of cooperation and because they give some indication
of the benefits one’s partner could provide in the future.
However, many factors can influence correlations between a partner’s
past behaviors and future behaviors. Most notably, a partner can find more
beneficial relationships or better outside options. Thus, paying attention to
cues of such changes in a partner can also be important in helping individuals avoid exploitation. In this regard, researchers have begun to explore
another set of inputs also derived directly from game theoretic models

Cooperative Relationships

5

of cooperation. In such models, inequalities between three variables—the
benefits and costs of cooperation and the expected duration of a series of
interactions—determines when it is reasonable to cooperate with another.
In most game theoretic formulations, the third variable—the expected
duration of the relationship—is defined a priori. However, partners can
often dramatically prolong or shorten a relationship by their own actions.
In such cases, individuals should be particularly attuned to their partner’s
commitment to continuing the relationship.
This second concern about the expected duration of a relationship has led
to exciting research in the last decade, showing that evaluations of a relationship depend on much more than the direct benefits one has received or
the costs one has incurred. Among other things, these evaluations depend on
cues that one’s partner can rely on a third party to satisfy the same needs as
one might provide (Niiya & Ellsworth, 2010). They can also depend on cues
that a partner is giving exclusive attention to the relationship (Ohtsubo et al.,
2014) or how a partner ranks the relationship relative to other possible relationships (DeScioli & Kurzban, 2009). What unifies these diverse cues and
signals is that each gives some indication of how much a partner values this
relationship specifically and how much that partner would work to extend it
into the future. Interestingly, having a partner who wants to prolong a beneficial relationship with you can also make that partner more valuable to you, a
process that can lead to mutually reinforcing evaluations among two partners
(Tooby & Cosmides, 1996).
Another area of active research aims to understand how individuals
encode the value of their partners and relationships and how these encodings, sometimes known as internal regulatory variables, shape future action.
Modeling work suggests that long-run encodings of a partner and the
partner’s contribution to one’s well-being can facilitate the emergence
of cooperative relationships (Hruschka & Henrich, 2006; Roberts, 2005).
There is also considerable theoretical work on what specific inputs should
contribute to these encodings, what “bookkeeping” styles transform these
inputs into encodings, and how these encodings interact with other information to guide decisions (Delton & Robertson, 2016; Hruschka et al., 2015;
Lieberman, Tooby, & Cosmides, 2007; Silk, 2003).
One concept that researchers from a range of fields have begun to explore
is perceived closeness, an assessment of relationship quality that in many
(but not all) cultures relies on a metaphor of spatial proximity (Aron et al.,
1992; Curry, Roberts, & Dunbar, 2013; Hackman et al., 2017; Jones & Rachlin,
2006). It is important to point out that perceived closeness is only loosely
correlated with actual spatial proximity. Rather, it is a general way of ranking relationships in terms of their positive value to an individual. Research
across neuroscience, economics, psychology, and anthropology has shown

6

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

that perceived closeness increases over a series of cooperative interactions
(Krueger et al., 2007), that cues that a partner devalues a relationship can
reduce feelings of closeness (Niiya & Ellsworth, 2010), and that perceived
closeness is associated with greater helping and sharing in experimental and
observational settings (Aron et al., 1992; Curry et al., 2013; Hackman et al.,
2017; Hruschka, 2010; Jones & Rachlin, 2006).
Although perceived closeness does somewhat correlate with a range
of other cues, such as genetic relatedness, past help, and frequency of
interaction, there is little evidence that perceived closeness can be reduced
to a simple mediator of other commonly studied variables. For example, the
associations of perceived closeness with helping are independent of other
important variables, such as genetic or affinal kinship, spatial proximity,
and help received from one’s partner (Hackman et al., 2017). Moreover, a
number of studies have shown that other important predictors, such as
genetic kinship and participation in a mating tie, have associations with
helping and sharing that are independent of perceived closeness (Curry
et al., 2013; Hackman, Danvers, & Hruschka, 2015). Thus, perceived closeness
appears to open a window to psychological mediators having independent
associations with cooperative behaviors.
Perceived closeness is one of the most thoroughly and widely studied
ways of encoding the value of a partner and relationship, but there are
many others, including judgments of competence, trustworthiness, genetic
kinship, mating and pair-bonding potential, and relative need, that likely
interact with closeness in guiding decisions about behavior toward partners
(Fiske, Cuddy, & Glick, 2007; Hackman et al., 2015; Rousseau, Sitkin, Burt, &
Camerer, 1998).
HOW CAN RESEARCHERS ASSESS THESE ALGORITHMS ACROSS CULTURE AND TAXA?
Taking an algorithmic approach to cooperative relationships has traditionally
required inferring relevant inputs and internal computations from observed
behaviors—attention, tolerance, affiliation, helping, and sharing (Silk et al.,
2013), and among humans, self-reports of internal states (Aron et al., 1992;
Hackman et al., 2017; Jones & Rachlin, 2006). Using averages and correlations
among these behaviors over time and behavioral responses to experimental
stimuli, researchers attempt to reverse engineer the relevant inputs, encodings, and algorithms that guide actions with relationship partners.
Among humans, self-reports about perceptions and evaluations provide
some window into the ways that individuals encode important information
about partners and relationships (Aron et al., 1992; Berscheid et al., 1989;
Jones & Rachlin, 2006). However, it is difficult to verify self-reports about
mental states. Moreover, it is not straightforward to elicit these self-reports

Cooperative Relationships

7

across diverse languages and cultures. For example, the increasingly
popular perceived closeness paradigm often uses spatial metaphors to elicit
rankings of relationships, but not all languages and cultures use spatial
metaphors to rank their relationships. And even when individuals may be
able to respond in terms of spatial metaphors, common two-dimensional
tasks on paper may not be immediately meaningful to respondents. In
the last 15 years, researchers have been developing and adapting different
approaches to tackle these problems and to develop a richer view of relational decision-making. These include experiments that probe attention,
processing speed, and decision-making about partners and relationships
(Karremans & Aarts, 2007; O’Gorman & Roberts, 2017; Wiltermuth & Heath,
2009), new methods for mapping brain networks involved in relational
decision-making (Krueger et al., 2007), and the development of locally
appropriate tools for assessing concepts such as emotional closeness and
their relationship with behavior in diverse cultural settings (Hackman et al.,
2017).
Researchers working with nonhuman animals have necessarily focused
on reverse engineering relevant inputs and algorithms from observable
behaviors and vocal signals. These include social proximity, social grooming, the amount of time spent in interactions, responses to separations and
reunions, reconciliation, behavioral synchrony, directed vocal exchanges,
and social monitoring, as well as researcher-derived indices of relationship
quality based on these behaviors (Cords & Aureli, 2000; Dunbar & Shultz,
2010; Silk et al., 2013). The challenge with many of these measures is that
they are both potentially inputs to and outcomes of decision-making algorithms. Thus, it can be difficult to determine whether these measures are
correlated with other relational outcomes because they are something that
individuals use as inputs to their future behaviors or simply because they
are both outcomes of underlying and unmeasured relational processes. For
example, Dunbar and Shultz argue that relationship longevity may not be
a good measure of relationship quality because it is a functional outcome
of relational processes (Dunbar & Shultz, 2010). However, it is also possible
that relationship longevity is a salient input in an individual’s evaluation of
a relationship that influences future decision-making. This may very likely
be the case in many human friendships (Hruschka, 2010). Unfortunately,
focusing solely on the correlations of relational length with other behavioral
outcomes cannot adjudicate between these two possibilities. Experimental
manipulation of social situations and partner behaviors, such as experiments
playing back calls from known partners, may provide one path forward in
determining what informs future decision-making in relationships and what
simply arises as an outcome of relational processes (Silk et al., 2013). Such
experimental approaches have been quite successful in human studies. And

8

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

in both human and nonhuman studies assays of hormonal mediators can
shed light on the internal processes underlying behaviors with relationship
partners (De Dreu, 2012).
PROMISING NEW DIRECTIONS
Progress in answering the broad questions outlined above will benefit from
greater cross-talk between the diverse biological, social, and behavioral fields
that have developed theories and methods for studying cooperative relationships. Among the many avenues for future research, we outline several that
should lead to novel insights into the origins, development, and persistence
of cooperative relationships.
THE TRANSITION TO A RELATIONSHIP
Numerous studies have shown that partners in long-term cooperative
relationships interact in very different ways than acquaintances. These
differences appear to rely on different inputs and algorithms for dealing
with a specific partner—for example, increasingly complex models of
one’s partner, common expectations and tools of coordination, increasing
preferences for the partner’s well-being, more concern about partner intent,
and less concern about individual acts of helping and reciprocation (Clark &
Mils, 1993; Hruschka, 2010; Xue, 2013; Xue & Silk, 2012). They can also lead
to different weighting of inputs. For example, partners in close relationships
might weight their own costs less and their partner’s benefits more than
strangers (McGuire, 2003). These changes involve a shift toward the relationship becoming an independent “thing” with emergent properties that
guide partners’ behaviors in the relationship. But what meta-inputs lead to
such a transition, and how does this transition take place? What cues and
signals do partners use to determine that their relationship is in a new state
and that they would benefit from different modes of decision-making? Some
research has shown, for example, that starting small and gradually raising
the stakes can be an effective route to increasing the levels of cooperation
in a relationship (Roberts & Renwick, 2003). However, it is not clear that
this captures the apparently qualitative changes that can occur in some
kinds of cooperative relationships (e.g., close friendships among humans)
(Hruschka, 2010; Xue & Silk, 2012).
THE ONTOGENY OF COOPERATIVE RELATIONSHIPS
In addition to the changes that occur within developing relationships,
individual organisms must also develop the capacities to cultivate and

Cooperative Relationships

9

maintain these relationships. Some evidence suggests that human children
across a range of settings follow similar trajectories between the ages of
7 and 15 in developing key understandings of shared expectations and
relational norms (Gummerum & Keller, 2008). At the same time, there can
be quite substantial variation in individual views about expectations for
a good friendship even within cultures (Hruschka, 2009). While studies
assessing actual relational behavior either experimentally or observationally
have mainly been restricted to populations in the United States, Europe,
and a few other high-income countries, this is now beginning to change.
Experimental work conducted in six different cultures, including several
small-scale societies, showed that children’s behavior in situations that
require self-sacrifice changes as children mature and varies across cultures.
Population-specific variation in altruistic behavior emerges during middle
childhood, and children converge toward the behavior of adults within
their groups (House et al., 2013). This raises important questions about the
generalizability of existing findings, and the degree to which the development of capacities for cooperative relationships relies on culturally specific
inputs.
CONDITIONS FAVORING COOPERATIVE RELATIONSHIPS
Cooperative relationships are only one of many ways to facilitate cooperation. Reputation and third-party punishment can help reinforce cooperation
among a wide range of partners (Fehr & Fischbacher, 2004; Milinski,
Semmann, & Krambeck, 2002). And modern markets backed by sophisticated legal systems permit individuals and organizations to cooperate in
relatively anonymous and arms-length interactions (Kranton, 1996). Under
what conditions do cooperative relationships become the more effective
option for facilitating cooperation? In biology, a great deal of modeling
and empirical work has examined these different systems in isolation, but
has not examined when we should see certain systems favored over others
(Hruschka & Henrich, 2006; Milinski et al., 2002; Noë & Hammerstein,
1995). Some modeling and experimental work in economics, sociology, and
anthropology has explored the social ecological conditions under which
rational individuals would devote effort to cultivating relationships versus
engaging in market-type exchanges (Kranton, 1996). Further work that
assesses these and other hypotheses using cross-cultural and cross-species
data should improve our understanding of the role of cooperative relationships in facilitating cooperation and also develop better integrated
theories of the conditions favoring different mechanisms for facilitating
cooperation.

10

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

FROM COOPERATIVE RELATIONSHIPS TO COOPERATIVE NETWORKS
Once cooperative relationships become emergent entities, then networks
of such relationships can become important in integrating larger groups of
individuals and creating novel opportunities for cooperation and exchange.
For the last several decades researchers have been exploring the emergent
properties of these networks and how they influence individual behaviors
and outcomes. These include the advantages in accessing information,
support, and economic resources by actors in specific network positions
(Burt, 2009; Jackson, 2008), the ways that network structure can facilitate
or discourage cooperation among dyads (Fehl, van der Post, & Semmann,
2011), and the role that network structure and density play in cumulative
cultural evolution (Chapais, 2009; Hill, Wood, Baggio, Hurtado, & Boyd,
2014). Social network analyses have also become an increasingly important
tool for mapping and measuring the structure of connections in nonhuman
taxa (Croft, James, & Krause, 2008).
CULTURAL TOOLS AND EVOLVED PSYCHOLOGY
Comparative work suggests that the capacity and motivation to cultivate
and maintain long-term cooperative relationships is a human universal,
and may be characteristic of the common ancestors of many primate species
(Hruschka, 2010; Seyfarth & Cheney, 2012). Irrespective of whether or not
this reflects psychological mechanisms that were selected specifically for the
cultivation of cooperative relationships, we do know that humans often rely
on cultural tools to build, regulate, and maintain these relationships. There
are numerous disparate examples of such cultural tools, including formal
written contracts between firms, material artifacts used as enduring symbols
for a relationship, stories and memories used to remind partners of the value
of the relationship, and rituals used to create community enforcement and
fear of supernatural punishment (Hruschka, 2010). Conversely, research on
culturally dependent relationships, such as contract-based cooperative ties
between firms, also shows that such relationships frequently rely on key
stakeholders who use common human capacities to cultivate friendships,
loyalty, and commitment (Uzzi, 1997). Thus, particularly in the human case,
a productive question is how do pre-existing capacities interact with cultural
tools to permit novel cooperative relationships between individuals as well
as higher level entities such as tribes, business firms, and nation-states?
In closing, the biological, social, and behavioral sciences have built promising empirical and theoretical inroads for studying the emergent properties of
cooperative relationships and how these emergent properties shape future
behaviors and outcomes. However, substantial progress can be made by
comparing and cross-fertilizing methods and theories across these diverse

Cooperative Relationships

11

fields. Here, we outline two potentially unifying concepts—inputs and
algorithms—and illustrate how one can productively frame past findings
and new directions in terms of these concepts. These and other tools for
building bridges across the rich bodies of work on cooperative relationships
should contribute to answering key questions and opening up new directions
about how such relationships emerge and shape individual behavior.
REFERENCES
Aron, A., Aron, E. N., & Smollan, D. (1992). Inclusion of other in the self scale and
the structure of interpersonal closeness. Journal of Personality and Social Psychology,
63, 596.
Berkman, L. F., & Glass, T. (2000). Social integration, social networks, social support,
and health. Social Epidemiology, 1, 137–173.
Berscheid, E., Snyder, M., & Omoto, A. M. (1989). The relationship closeness inventory: Assessing the closeness of interpersonal relationships. Journal of Personality
and Social Psychology, 57, 792.
Boyd, R., & Richerson, P. J. (2009). Culture and the evolution of human cooperation.
Philosophical Transactions of the Royal Society of London B: Biological Sciences, 364,
3281–3288.
Brent, L. J., Chang, S. W., Gariépy, J. F., & Platt, M. L. (2014). The neuroethology of
friendship. Annals of the New York Academy of Sciences, 1316, 1–17.
Brosnan, S. F., Silk, J. B., Henrich, J., Mareno, M. C., Lambeth, S. P., & Schapiro, S. J.
(2009). Chimpanzees (Pan troglodytes) do not develop contingent reciprocity in an
experimental task. Animal Cognition, 12, 587–597.
Burt, R. S. (2009). Structural holes: The social structure of competition. Cambridge, MA:
Harvard University Press.
Chapais, B. (2009). Primeval kinship: How pair-bonding gave birth to human society. Cambridge, MA: Harvard University Press.
Clark, M. S., & Mils, J. (1993). The difference between communal and exchange
relationships: What it is and is not. Personality and Social Psychology Bulletin, 19,
684–691.
Cords, M., & Aureli, F. (2000). Reconciliation and relationship qualities. In F. Aureli
& F. B. M. de Waal (Eds.), Natural conflict resolution (pp. 177–198). Berkeley: University of California Press.
Croft, D. P., James, R., & Krause, J. (2008). Exploring animal social networks. Princeton,
NJ: Princeton University Press.
Curry, O., Roberts, S. G., & Dunbar, R. I. (2013). Altruism in social networks: Evidence
for a ‘kinship premium’. British Journal of Psychology, 104, 283–295.
De Dreu, C. K. (2012). Oxytocin modulates cooperation within and competition
between groups: An integrative review and research agenda. Hormones and Behavior, 61, 419–428.
Delton, A. W., & Robertson, T. E. (2016). How the mind makes welfare tradeoffs:
Evolution, computation, and emotion. Current Opinion in Psychology, 7, 12–16.

12

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

DeScioli, P., & Kurzban, R. (2009). The alliance hypothesis for human friendship.
PLoS One, 4, e5802.
Dunbar, R. I., & Shultz, S. (2010). Bondedness and sociality. Behaviour, 147,
775–803.
Dyer, J. H., & Singh, H. (1998). The relational view: Cooperative strategy and sources
of interorganizational competitive advantage. Academy of Management Review, 23,
660–679.
Fehl, K., van der Post, D. J., & Semmann, D. (2011). Co-evolution of behaviour
and social network structure promotes human cooperation. Ecology Letters, 14,
546–551.
Fehr, E., & Fischbacher, U. (2004). Third-party punishment and social norms. Evolution and Human Behavior, 25, 63–87.
Fiske, S. T., Cuddy, A. J., & Glick, P. (2007). Universal dimensions of social cognition:
Warmth and competence. Trends in Cognitive Sciences, 11, 77–83.
Gummerum, M., & Keller, M. (2008). Affection, virtue, pleasure, and profit: Developing an understanding of friendship closeness and intimacy in western and Asian
societies. International Journal of Behavioral Development, 32, 218–231.
Hackman, J., Danvers, A., & Hruschka, D. J. (2015). Closeness is enough for friends,
but not mates or kin: Mate and kinship premiums in India and US. Evolution and
Human Behavior, 36, 137–145.
Hackman, J., Munira, S., Jesmin, K., & Hruschka, D. J. (2017). Revisiting psychology
mechanisms in the anthropological study of altruism. Human Nature, 28(1), 76–91.
Hill, K. R., Wood, B. M., Baggio, J., Hurtado, A. M., & Boyd, R. T. (2014).
Hunter-gatherer inter-band interaction rates: Implications for cumulative culture.
PLoS One, 9, e102806.
Hinde, R. A. (1976). Interactions, relationships and social structure. Man, 11, 1–17.
House, B. R., Silk, J. B., Henrich, J., Barrett, H. C., Scelza, B. A., Boyette, A. H., …
Laurence, S. (2013). Ontogeny of prosocial behavior across diverse societies. Proceedings of the National Academy of Sciences, 110, 14586–14591.
Hruschka, D. (2009). Defining cultural competence in context: Dyadic norms of
friendship among US high school students. Ethos, 37, 205–224.
Hruschka, D. J. (2010). Friendship: Development, ecology, and evolution of a relationship
(Vol. 5). Berkeley: Univ of California Press.
Hruschka, D., Hackman, J., & Macfarlan, S. (2015). Why do humans help their
friends? Proximal and ultimate hypotheses from evolutionary theory. In V.
Zeigler-Hill, L. L. M. Welling & T. K. Shackelford (Eds.), Evolutionary perspectives
on social psychology (pp. 255–266). New York, NY: Springer.
Hruschka, D. J., & Henrich, J. (2006). Friendship, cliquishness, and the emergence of
cooperation. Journal of Theoretical Biology, 239, 1–15.
Jackson, M. O. (2008). Social and economic networks (Vol. 3). Princeton, NJ: Princeton
University Press.
Jaeggi, A. V., & Gurven, M. (2013). Reciprocity explains food sharing in humans and
other primates independent of kin selection and tolerated scrounging: A phylogenetic meta-analysis. Proceedings of the Royal Society of London B: Biological Sciences,
280, 20131615.

Cooperative Relationships

13

Jones, B., & Rachlin, H. (2006). Social discounting. Psychological Science, 17,
283–286.
Karremans, J. C., & Aarts, H. (2007). The role of automaticity in determining the inclination to forgive close others. Journal of Experimental Social Psychology, 43, 902–917.
Kranton, R. E. (1996). Reciprocal exchange: A self-sustaining system. The American
Economic Review, 86(4), 830–851.
Krueger, F., McCabe, K., Moll, J., Kriegeskorte, N., Zahn, R., Strenziok, M., … Grafman, J. (2007). Neural correlates of trust. Proceedings of the National Academy of
Sciences, 104, 20084–20089.
Kummer, H. (1978). On the value of social relationships to nonhuman primates: A
heuristic scheme. Social Science Information/sur les sciences sociales, 17(4–5), 687–705.
Leeds, B. A. (2003). Alliance reliability in times of war: Explaining state decisions to
violate treaties. International Organization, 57, 801–827.
Lieberman, D., Tooby, J., & Cosmides, L. (2007). The architecture of human kin detection. Nature, 445, 727–731.
McGuire, A. M. (2003). “It was nothing”-Extending evolutionary models of altruism
by two social cognitive biases in judgments of the costs and benefits of helping.
Social Cognition, 21, 363.
Milinski, M., Semmann, D., & Krambeck, H.-J. (2002). Reputation helps solve the
‘tragedy of the commons’. Nature, 415, 424–426.
Niiya, Y., & Ellsworth, P. C. (2010). Acceptability of favor requests in the United States
and Japan. Journal of Cross-Cultural Psychology, 43, 273–285.
Noë, R., & Hammerstein, P. (1995). Biological markets. Trends in Ecology & Evolution,
10, 336–339.
O’Gorman, R., & Roberts, R. (2017). Distinguishing family from friends: Implicit cognitive differences regarding general dispositions, attitude similarity, and group
membership. Human Nature.
Ohtsubo, Y., Matsumura, A., Noda, C., Sawa, E., Yagi, A., & Yamaguchi, M. (2014).
It’s the attention that counts: Interpersonal attention fosters intimacy and social
exchange. Evolution and Human Behavior, 35, 237–244.
Ring, P. S., & Van de Ven, A. H. (1992). Structuring cooperative relationships between
organizations. Strategic Management Journal, 13, 483–498.
Roberts, G. (2005). Cooperation through interdependence. Animal Behaviour, 70,
901–908.
Roberts, G., & Renwick, J. S. (2003). The development of cooperative relationships:
An experiment. Proceedings of the Royal Society of London B: Biological Sciences, 270,
2279–2283.
Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after
all: A cross-discipline view of trust. Academy of Management Review, 23, 393–404.
Seyfarth, R. M., & Cheney, D. L. (2012). The evolutionary origins of friendship.
Annual Review of Psychology, 63, 153–177.
Silk, J. B. (2003). Cooperation without counting. Genetic and Cultural Evolution of Cooperation, 37–54.
Silk, J. B. (2014). Evolutionary perspectives on the links between close social bonds,
health, and fitness. In M. Weinstein & M. A. Lane (Eds.), Committee on population,

14

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

division of behavioral and social sciences and education. Washington D.C.: The National
Academies Press.
Silk, J., Cheney, D., & Seyfarth, R. (2013). A practical guide to the study of
social relationships. Evolutionary Anthropology: Issues, News, and Reviews, 22,
213–225.
Tooby, J., & Cosmides, L. (1996). Friendship and the banker’s paradox: Other pathways to the evolution of adaptations for altruism. Proceedings of the British Academy,
88, 119–144.
Tooby, J., Cosmides, L., Sell, A., Lieberman, D., & Sznycer, D. (2008). Internal regulatory variables and the design of human motivation: A computational and evolutionary approach. In A. J. Elliot (Ed.), Handbook of approach and avoidance motivation
(pp. 251–271). Mahwah, NJ: Lawrence Erlbaum.
Uzzi, B. (1997). Social structure and competition in interfirm networks: The paradox
of embeddedness. Administrative Science Quarterly, 42(1), 35–67.
West, S. A., Fisher, R. M., Gardner, A., & Kiers, E. T. (2015). Major evolutionary
transitions in individuality. Proceedings of the National Academy of Sciences, 112,
10112–10119.
Wiltermuth, S. S., & Heath, C. (2009). Synchrony and cooperation. Psychological Science, 20, 1–5.
Xue, M. (2013). Altruism and reciprocity among friends and kin in a Tibetan village.
Evolution and Human Behavior, 34, 323–329.
Xue, M., & Silk, J. B. (2012). The role of tracking and tolerance in relationship among
friends. Evolution and Human Behavior, 33, 17–25.

DANIEL HRUSCHKA SHORT BIOGRAPHY
Daniel J. Hruschka is an Associate Professor of Anthropology and Global
Health at Arizona State University. Among other interests, he studies how
humans choose to help others, how they cultivate relationships that promote
cooperation, and when they choose to exclude outsiders. He is author of
the book, “Friendship: Development, Ecology and Evolution of a Relationship”. He directs the Laboratory of Culture Change and Behavior and his
research has been funded by the National Science Foundation and the Templeton Foundation.
JOAN SILK SHORT BIOGRAPHY
Joan B. Silk. I am interested in how natural selection shapes the evolution
of social behavior in primates. Most of my empirical work has focused
on the behavioral and reproductive strategies of female baboons (Papio
cynocephalus, Papio ursinus). Recent work documents the adaptive benefits
females derive from close social bonds. I am particularly interested in
questions that explicitly link studies of nonhuman primates to humans.

Cooperative Relationships

15

Experimental work conducted with chimpanzees and children focuses
on the phylogenetic origins and ontogenetic development of prosocial
preferences.
RELATED ESSAYS
Emergence of Stratification in Small Groups (Sociology), Noah Askin et al.
Aggression and Victimization (Psychology), Sheri Bauman and Aryn Taylor
Empathy Gaps between Helpers and Help-Seekers: Implications for Cooperation (Psychology), Vanessa K. Bohns and Francis J. Flynn
Close Friendships among Contemporary People (Sociology), Matthew E.
Brashears and Laura Aufderheide Brashears
Neighborhoods and Cognitive Development (Psychology), Jondou Chen and
Jeanne Brooks-Gunn
Misinformation and How to Correct It (Psychology), John Cook et al.
Bullying, Aggression, and Human Development (Psychology), Samuel E.
Ehrenreich and Marion K. Underwood
Diversity in Groups (Sociology), Catarina R. Fernandes and Jeffrey T. Polzer
Cognitive Processes Involved in Stereotyping (Psychology), Susan T. Fiske
and Cydney H. Dupree
State of the Art in Competition Research (Psychology), Márta Fülöp and
Gábor Orosz
Interdependence, Development, and Interstate Conflict (Political Science),
Erik Gartzke
Family Relationships and Development (Psychology), Joan E. Grusec
Food Sharing (Anthropology), Michael Gurven and Adrian V. Jaeggi
Grandmothers and the Evolution of Human Sociality (Anthropology), Kristen
Hawkes and James Coxworth
Group Identity and Political Cohesion (Political Science), Leonie Huddy
The Development of Social Trust (Psychology), Vikram K. Jaswal and Marissa
B. Drell
Social Network Analysis in the Study of Ethnic Inequalities (Psychology),
Frank Kalter
Reconciliation and Peace-Making: Insights from Studies on Nonhuman
Animals (Anthropology), Sonja E. Koski
Cooperative Breeding and Human Evolution (Anthropology), Karen L.
Kramer
Exploring Opportunities in Cultural Diversity (Political Science), David D.
Laitin and Sangick Jeon
Empirical Models of Bilateral Contracting (Economics), Robin S. Lee
Against Game Theory (Psychology), Gale M. Lucas et al.
Emotion and Intergroup Relations (Psychology), Diane M. Mackie et al.

16

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Social-Emotional Responding: A Perspective from Developmental Psychology (Psycholocy), Tina Malti, Stefania Sette, and Sebastian P. Dys
Domestic Political Institutions and Alliance Politics (Political Science),
Michaela Mattes
Political Psychology and International Conflict (Sociology), Rose McDermott
Implicit Attitude Measures (Psychology), Gregory Mitchell and Philip E.
Tetlock
Why Do Governments Abuse Human Rights? (Political Science), Will H.
Moore and Ryan M. Welch
A Bio-Social-Cultural Approach to Early Cognitive Development: Entering
the Community of Minds (Psychology), Katherine Nelson
Perceptions of the Legitimacy of Collective Decisions (Political Science),John
W. Patty
Class, Cognition, and Face-to-Face Interaction (Sociology), Lauren A. Rivera
The Economics of Conflict and Peace (Economics), Dominic Rohner
Creativity in Teams (Psychology), Leigh L. Thompson and Elizabeth Ruth
Wilson
Social Neuroendocrine Approaches to Relationships (Sociology), Sari M. van
Anders and Peter B. Gray
Theory of Mind (Psychology), Henry Wellman